In classification, imbalance occurs when at least one class (the majority class) contains a higher number of samples than the others. The classes with the lowest number of samples are called minority classes. If uncorrected, conventional supervised loss functions, such as (multi-class) cross-entropy, skew the learning process in favor of the majority class, introducing bias and poor generalization toward the minority class samples [1]}, [2]}. Imbalance approaches are categorized into three groups: data-level, algorithm-level, and hybrid. Data-level strategies manipulate and create new data points to equalize data sampling. Popular data-level methods include Random Over-Sampling (ROS) and Random Under-Sampling (RUS) [3]}. ROS randomly resamples data points from the minority classes, while RUS randomly leaves out a randomly selected portion of the majority classes to decrease imbalance levels. Algorithm-level strategies use regularization or minimization of loss/cost functions. Weighted loss is a common approach where each sample's loss is weighted by the inverse frequency of that sample's class. Focal loss [4]} is another type of cost function that has seen wide success. Hybrid methods combine one or more types of strategies (e.g. Two-Phase Training, [5]}).
